academic-skills

End-to-end skills for academic writing, data analysis, teaching, and research communication.

Prerequisites

• •Claude Code or compatible AI coding assistant
• •Python 3.8+ (for script-based skills)
• •pandas, openpyxl (for data processing skills)
• •Node.js (for npx-based installation)

Installation

Choose one of the methods below.

Option 1: Ask the Agent (Beginner-Friendly)

No coding experience required. If you are new to Claude Code or not comfortable with the command line, this is the easiest way to get started. Just open Claude Code and type:

Please install skills from github.com/TyrealQ/q-skills

Claude will handle the installation for you — no terminal, no commands.

Option 2: Quick Install

Requires Node.js for npx:

npx skills add TyrealQ/q-skills

Option 3: Plugin Marketplace

Register q-skills as a plugin source in Claude Code, then install all or selected skills.

Step 1 — Register (run once inside Claude Code):

/plugin marketplace add TyrealQ/q-skills

Step 2 — Install:

/plugin install q-skills@q-skills

Migrating from older installs? If you previously installed academic-skills@q-skills, visual-content-skills@q-skills, or utility-skills@q-skills, uninstall them first, then install the unified q-skills@q-skills plugin above.

Option 4: Manual (Clone and Copy)

git clone https://github.com/TyrealQ/q-skills.git

Windows (PowerShell):

Copy-Item -Recurse -Force q-skills\skills\* $env:USERPROFILE\.claude\skills\

macOS/Linux:

cp -r q-skills/skills/* ~/.claude/skills/

Note: The exact skills path depends on your AI assistant. Common locations: ~/.claude/skills/, ~/.gemini/skills/

Update Skills

Via Plugin UI (Recommended)

1. Run /plugin in Claude Code
2. Switch to the Marketplaces tab (arrow keys or Tab)
3. Select q-skills
4. Choose Update marketplace

You can also enable auto-update to receive the latest versions automatically.

Force Reinstall

npx skills add TyrealQ/q-skills --force

Manual Update

cd q-skills
git pull

Then re-copy the skills to your skills directory (see Manual install above).

Available Skills

Academic Skills

Visual Content Skills

Utility Skills

Skill Details

q-scholar

Academic manuscript writing suite for drafting journal-ready prose following APA 7th edition standards. Orchestrates specialized sub-skills for complete manuscript preparation workflows.

Sub-Skills:

Triggers:

• •"Help me write the methods and results for my study"
• •"Draft a results section for this analysis"
• •"Analyze this dataset and generate descriptive statistics"

Features:

• •End-to-end manuscript support (exploratory data analysis -> methods -> results)
• •APA 7th edition formatting (tables, statistics, notation)
• •Narrative prose style (no bullet points or em-dashes)
• •Shared style guides and templates
• •Appendix strategies for technical details

Folder Structure:

q-scholar/
|-- SKILL.md                              # Orchestration skill
|-- references/                           # Shared style guides
|   |-- apa_style_guide.md                # Numbers, statistics, notation, formulas
|   |-- table_formatting.md               # APA 7th table examples
|   `-- appendix_template.md              # Shared appendix structure (methods + results)
|-- q-eda/
|   |-- SKILL.md                          # Data exploration skill
|   |-- scripts/                          # run_eda.py
|   `-- references/                       # Interview protocol, invocation guide, summary template + instructions
|-- q-intro/
|   |-- SKILL.md                          # Introduction drafting skill
|   `-- references/                       # Template and interview questions
|-- q-litreview/
|   |-- SKILL.md                          # Literature review drafting skill
|   `-- references/                       # Template and interview questions
|-- q-methods/
|   |-- SKILL.md                          # Methods drafting skill
|   `-- references/                       # Methods template
|-- q-multimodal/
|   |-- SKILL.md                          # Multimodal feature extraction skill
|   |-- scripts/                          # pillow/, opensmile/, gemini/ (batch + standard)
|   `-- references/                       # Feature definitions, Gemini workflows, checkpoint format
|-- q-results/
|   |-- SKILL.md                          # Results drafting skill
|   `-- references/                       # Results template
`-- q-tf/
    |-- SKILL.md                          # Topic finetuning skill
    |-- scripts/                          # classify_outliers.py, plan & Excel updaters
    `-- references/                       # Code patterns, preservation rules, outlier workflow, worked example

Example:

Help me write the methods and results sections for my topic modeling study on esports discourse

q-infographics

Convert documents into compelling business stories and cartoon-style infographics. Image generation defaults to OpenAI GPT Image 2 with a Gemini fallback; story generation uses Gemini.

Triggers:

• •"Create an infographic from this document..."
• •"Convert this paper to a visual summary..."
• •"Generate a business story from..."

Features:

• •Two-stage pipeline: Document -> Story -> Infographic
• •Business story style (36Kr/Huxiu format) with "golden sentences"
• •Hand-drawn cartoon-style infographics (16:9)
• •Automatic logo branding on generated infographics
• •Review checkpoints at each stage
• •Supports PDF, DOCX, and text input (via markitdown)

Requirements:

• •pip install openai google-genai Pillow python-dotenv markitdown
• •OPENAI_API_KEY (for default gpt-image-2 image generation) and GEMINI_API_KEY (for story generation; also for the Gemini image fallback). Select the image backend via IMAGE_MODEL=gpt|gemini or --model gpt|gemini. See Environment Configuration.

Folder Structure:

q-infographics/
|-- SKILL.md                              # Main skill file
|-- assets/
|   `-- Logo_Q.png                        # Brand logo, auto-overlaid on infographics
|-- references/
|   |-- story.txt                         # Story generation prompt
|   |-- image.txt                         # Infographic generation prompt
|   `-- prompts_reference.md              # Prompt descriptions and key elements
|-- scripts/
|   |-- gen_story.py                      # Story generator script
|   `-- gen_image.py                      # Image generator script
# Sample outputs → see illustrations/q-infographics/ at repo root

Example:

Create an infographic from my research paper on gamification in esports

Sample Outputs:

!DIGITAL_ENTREPRENEURSHIP_INFO1

!DIGITAL_ENTREPRENEURSHIP_INFO2

q-presentations

Convert content into branded slide decks with 16 visual style presets, layout-driven overlay safety, and automatic logo branding. Fork of baoyu-slide-deck with video-overlay-aware layout.

Triggers:

• •"Create a slide deck from this content..."
• •"Make a presentation about..."
• •"Generate slides for my talk..."

Features:

• •16 style presets (blueprint, chalkboard, corporate, minimal, sketch-notes, watercolor, etc.)
• •Composable dimension system (texture + mood + typography + density)
• •Video-overlay-aware layout: internal layout-driven overlay-safe selection
• •Automatic Dr. Q logo branding with configurable placement and auto-invert for dark styles
• •Image generation via OpenAI GPT Image 2 by default, with Gemini (gemini-3-pro-image-preview) available as a fallback
• •PPTX and PDF export
• •Partial workflows (outline-only, prompts-only, regenerate specific slides)

Requirements:

• •pip install openai google-genai Pillow python-dotenv
• •OPENAI_API_KEY (for default gpt-image-2) or GEMINI_API_KEY (when IMAGE_MODEL=gemini / --model gemini)
• •Bun available for PPTX/PDF merge scripts (npx -y bun ...)

Folder Structure:

q-presentations/
|-- SKILL.md                              # Main skill file
|-- assets/
|   `-- Logo_Q.png                        # Brand logo, auto-overlaid on slides
|-- references/
|   |-- base-prompt.md                    # Image generation base prompt
|   |-- design-guidelines.md              # Typography, colors, visual hierarchy
|   |-- layouts.md                        # 28 layout types
|   |-- outline-template.md               # Outline structure template
|   |-- config/preferences-schema.md      # EXTEND.md user preferences
|   |-- dimensions/                       # Composable style dimensions (5 files)
|   `-- styles/                           # 22 style definitions
`-- scripts/
    |-- gen_slide.py                      # Image generation (GPT Image 2 default; Gemini via --model gemini)
    |-- overlay_logo.py                   # Logo overlay with auto-invert
    |-- merge-to-pptx.ts                  # PPTX merge (Bun/TS)
    `-- merge-to-pdf.ts                   # PDF merge (Bun/TS)
# Sample outputs → see illustrations/q-presentations/ at repo root

Example:

Create a chalkboard-style slide deck from my research paper on AI agents

Sample Outputs:

!01-slide-cover

!02-slide-authenticity-trap

!03-slide-two-playbooks

q-educator

Course content development skill for university teaching workflows. Produces interview-driven lecture outlines, demo plans, follow-up emails, assignment prompts, and per-group feedback.

Triggers:

• •"Help me design next week's lecture..."
• •"Draft an assignment prompt for this module..."
• •"Write feedback for each student group..."

Features:

• •Interview-first planning workflow before drafting
• •Projects-first teaching philosophy with domain-specific analogies
• •Structured deliverables for lecture, demo, email, assignment, and feedback
• •Iterative review checkpoints after each deliverable
• •Reference examples for assignments, lectures, emails, demos, and feedback

Folder Structure:

q-educator/
|-- SKILL.md
`-- references/
    |-- teaching_philosophy.md            # Six governing principles
    |-- interview_protocol.md             # Six-question interview sequence
    |-- lecture_template.md               # Lecture outline structure + design rules
    |-- demo_template.md                  # Demo outline structure + design rules
    |-- email_guidelines.md               # Follow-up email style rules
    |-- assignment_template.md            # Assignment prompt structure + design rules
    |-- feedback_template.md              # Per-group feedback structure + design rules
    |-- key_phrases.md                    # Philosophy catchphrases
    |-- lecture_example.md                # Example lecture outline
    |-- demo_example.md                   # Example demo outline
    |-- email_example.md                  # Example follow-up email
    |-- assignment_example.md             # Example assignment prompt
    `-- feedback_example.md               # Example per-group feedback

Example:

Help me build a week 6 lecture + demo + assignment plan for a graduate analytics course

commit

Stage and commit all uncommitted changes with smart file grouping and conventional commit messages. Analyzes changed files, groups by topic (content, skills, code, config), and generates descriptive commit messages.

Triggers:

• •/commit
• •"Commit my changes"

Features:

• •Automatic file classification by path pattern
• •Smart grouping: one commit per topic when changes span multiple areas
• •Conventional commit format (feat:, fix:, docs:, etc.)
• •Explicit file staging (never git add .)
• •Auto-cleanup of editor/build temp files after each commit

handoff

Capture the load-bearing outcomes of a session, route them to the right durable location (plan file, auto-memory, project CLAUDE.md, user CLAUDE.md), and produce a copy-pasteable resume prompt for a fresh session. Use before /compact or at the end of a working session.

Triggers:

• •/handoff
• •"Hand off this session" / "Wrap up"
• •"Update docs for next session"
• •Before running /compact

Features:

• •Four-step workflow: survey the session → locate destinations → apply updates → produce resume prompt
• •Destination routing table that places each outcome (decisions, conventions, lessons, banned terms) in its single durable home, never duplicated
• •Self-contained resume prompt under ~250 words with required sections (Context, Read first, Where we left off, Conventions to honor, Next task, Known gotchas)
• •Anti-patterns guardrail: no session logs in project files, no new doc locations without asking, no extracting unvalidated lessons

learn

Persist user preferences, styles, and behavioral patterns to ~/CLAUDE.md, ~/.claude/rules/, or project memory. Extracts corrections, explicit rules, and positive reinforcement from the current conversation, and answers read-only queries about what has already been remembered.

Triggers:

• •/learn
• •"Remember this preference" / "Save this to CLAUDE.md" / "Update CM"
• •"Always do X" / "Never do Y" / "From now on…"
• •"Forget X" / "What do you remember about me?"

Features:

• •Three-tier persistence: user instructions, user rules, project memory
• •Inline trigger taxonomy (explicit rules, corrections, positive reinforcement, domain context, style edits)
• •Repetition threshold: single off-hand corrections stay tentative until repeated
• •Conflict detection: contradicting preferences surface side-by-side for explicit approval
• •Query mode: read-only lookup, section-level quoting, and confirmed forgetting
• •Anti-patterns guardrail: never infers from silence, hypotheticals, or third-party preferences
• •Keeps ~/CLAUDE.md under 200 lines, migrating overflow to rule files

organize

Audit project structure, align it to documented conventions, and archive superseded content under _archive/. Works on any project type (research, content, software, data). Writes a plan file, asks on ambiguous calls, and hands off to /commit or /ship.

Triggers:

• •/organize
• •"Clean up this repo"
• •"Standardize folder structure"
• •"Archive old versions"

Features:

• •Four-detector audit: case drift, superseded generations, orphan files, tracked per-machine state
• •Project-agnostic target structure (folder roles, naming rules, _archive/ convention, .gitignore baseline)
• •Plan-first workflow — no moves until approved
• •Sync-safe moves (shutil.copytree + retry) for cloud-synced paths
• •Refreshes project-level docs file (structure diagram, conventions)
• •Hands off to /commit or /ship; never commits directly

ship

Full ship cycle: update documentation, stage, commit, and push to remote. Automatically updates CHANGELOG.md, CLAUDE.md, and READMEs affected by the current changes.

Triggers:

• •/ship
• •"Ship my changes"

Features:

• •Auto-updates CHANGELOG.md, CLAUDE.md, and relevant READMEs
• •Stale reference detection for deleted/renamed files
• •Smart commit grouping (same as commit skill)
• •Pushes to remote with upstream tracking
• •Auto-cleanup of editor/build temp files after push

Environment Configuration

Some skills need API keys:

• •OpenAI (OPENAI_API_KEY) — default image generation backend (gpt-image-2) used by q-presentations and q-infographics.
• •Google Gemini (GEMINI_API_KEY) — story generation in q-infographics, topic classification in q-tf, multimodal analysis in q-multimodal, and the image-generation fallback (gemini-3-pro-image-preview) when IMAGE_MODEL=gemini or --model gemini.

Getting Your API Keys

• •OpenAI: platform.openai.com/api-keys
• •Google Gemini: Google AI Studio

Setting the API Keys

Create a .env file in your project's working directory:

OPENAI_API_KEY=your-openai-key-here
GEMINI_API_KEY=your-gemini-key-here

Important: Add .env to your .gitignore so you don't accidentally commit your key:

```bash

echo ".env" >> .gitignore

```

All skills that use the Gemini API load this file automatically via python-dotenv. Alternatively, set the variable directly in your terminal:

macOS / Linux:

export GEMINI_API_KEY=your-api-key-here

Windows (PowerShell):

$env:GEMINI_API_KEY = "your-api-key-here"

Windows (Command Prompt):

set GEMINI_API_KEY=your-api-key-here

To make it permanent, add the export line to your shell profile (~/.bashrc, ~/.zshrc) or set it as a system environment variable on Windows.

Optional Variables

Acknowledgments

• •Inspired by baoyu-skills by Jim Liu
• •Built for use with Claude Code and compatible AI assistants

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! Please submit issues or pull requests.